Overview

Dataset statistics

Number of variables8
Number of observations693500
Missing cells2437725
Missing cells (%)43.9%
Duplicate rows56
Duplicate rows (%)< 0.1%
Total size in memory42.3 MiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

Dataset has 56 (< 0.1%) duplicate rowsDuplicates
reflectivity is highly overall correlated with total_powerHigh correlation
total_power is highly overall correlated with reflectivityHigh correlation
reflectivity has 586650 (84.6%) missing valuesMissing
total_power has 552515 (79.7%) missing valuesMissing
velocity has 645987 (93.1%) missing valuesMissing
spectrum_width has 652573 (94.1%) missing valuesMissing

Reproduction

Analysis started2024-04-22 10:14:46.375118
Analysis finished2024-04-22 10:14:56.719519
Duration10.34 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

Distinct627735
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.697573
Minimum7.968836
Maximum13.349576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 MiB
2024-04-22T17:14:56.808813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7.968836
5-th percentile8.7681769
Q110.027169
median10.682837
Q311.390367
95-th percentile12.592767
Maximum13.349576
Range5.38074
Interquartile range (IQR)1.3631988

Descriptive statistics

Standard deviation1.0911675
Coefficient of variation (CV)0.10200141
Kurtosis-0.26207496
Mean10.697573
Median Absolute Deviation (MAD)0.6818275
Skewness-0.032847474
Sum7418766.6
Variance1.1906465
MonotonicityNot monotonic
2024-04-22T17:14:56.945694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.65961 1387
 
0.2%
10.659622 11
 
< 0.1%
10.659617 9
 
< 0.1%
10.659519 8
 
< 0.1%
10.659632 7
 
< 0.1%
10.658484 7
 
< 0.1%
10.659332 7
 
< 0.1%
10.659641 7
 
< 0.1%
10.65975 7
 
< 0.1%
10.659512 6
 
< 0.1%
Other values (627725) 692044
99.8%
ValueCountFrequency (%)
7.968836 1
< 0.1%
7.969162 1
< 0.1%
7.969193 1
< 0.1%
7.970259 1
< 0.1%
7.9705744 1
< 0.1%
7.9715533 1
< 0.1%
7.9717216 1
< 0.1%
7.97197 1
< 0.1%
7.9725876 1
< 0.1%
7.972898 1
< 0.1%
ValueCountFrequency (%)
13.349576 1
< 0.1%
13.349453 1
< 0.1%
13.349276 1
< 0.1%
13.348269 1
< 0.1%
13.34808 1
< 0.1%
13.347501 1
< 0.1%
13.347272 1
< 0.1%
13.346938 1
< 0.1%
13.346316 1
< 0.1%
13.346261 1
< 0.1%

latitude
Real number (ℝ)

Distinct387885
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.7564
Minimum103.99045
Maximum109.46606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 MiB
2024-04-22T17:14:57.069782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum103.99045
5-th percentile104.771
Q1106.05762
median106.75066
Q3107.47169
95-th percentile108.69756
Maximum109.46606
Range5.475614
Interquartile range (IQR)1.4140775

Descriptive statistics

Standard deviation1.1224184
Coefficient of variation (CV)0.010513828
Kurtosis-0.30005601
Mean106.7564
Median Absolute Deviation (MAD)0.707512
Skewness-0.041862169
Sum74035562
Variance1.259823
MonotonicityNot monotonic
2024-04-22T17:14:57.195073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.728325 1387
 
0.2%
106.73381 22
 
< 0.1%
106.72947 21
 
< 0.1%
106.7268 20
 
< 0.1%
106.73201 18
 
< 0.1%
106.732925 18
 
< 0.1%
106.72451 18
 
< 0.1%
106.73116 17
 
< 0.1%
106.72284 17
 
< 0.1%
106.73006 16
 
< 0.1%
Other values (387875) 691946
99.8%
ValueCountFrequency (%)
103.99045 1
< 0.1%
103.990486 1
< 0.1%
103.991234 1
< 0.1%
103.991394 1
< 0.1%
103.99291 1
< 0.1%
103.99304 1
< 0.1%
103.99327 1
< 0.1%
103.99329 1
< 0.1%
103.99404 1
< 0.1%
103.9942 1
< 0.1%
ValueCountFrequency (%)
109.466064 1
< 0.1%
109.466 1
< 0.1%
109.46522 1
< 0.1%
109.46513 1
< 0.1%
109.46354 1
< 0.1%
109.46345 1
< 0.1%
109.46344 1
< 0.1%
109.46342 1
< 0.1%
109.4627 1
< 0.1%
109.46254 1
< 0.1%

altitude
Real number (ℝ)

Distinct23163
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7134.5652
Minimum10
Maximum25055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 MiB
2024-04-22T17:14:57.323156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile368
Q12396
median5784
Q310797
95-th percentile18434
Maximum25055
Range25045
Interquartile range (IQR)8401

Descriptive statistics

Standard deviation5732.4764
Coefficient of variation (CV)0.80347944
Kurtosis-0.039298383
Mean7134.5652
Median Absolute Deviation (MAD)3898
Skewness0.85495197
Sum4.9478209 × 109
Variance32861286
MonotonicityNot monotonic
2024-04-22T17:14:57.458194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1387
 
0.2%
42 299
 
< 0.1%
35 289
 
< 0.1%
26 252
 
< 0.1%
85 246
 
< 0.1%
47 237
 
< 0.1%
58 234
 
< 0.1%
25 199
 
< 0.1%
289 196
 
< 0.1%
60 194
 
< 0.1%
Other values (23153) 689967
99.5%
ValueCountFrequency (%)
10 1387
0.2%
13 39
 
< 0.1%
14 136
 
< 0.1%
15 101
 
< 0.1%
16 23
 
< 0.1%
17 34
 
< 0.1%
18 71
 
< 0.1%
19 61
 
< 0.1%
20 72
 
< 0.1%
21 66
 
< 0.1%
ValueCountFrequency (%)
25055 2
< 0.1%
24998 1
< 0.1%
24994 2
< 0.1%
24941 1
< 0.1%
24937 1
< 0.1%
24933 2
< 0.1%
24898 1
< 0.1%
24880 1
< 0.1%
24877 1
< 0.1%
24873 2
< 0.1%

reflectivity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5561
Distinct (%)5.2%
Missing586650
Missing (%)84.6%
Infinite0
Infinite (%)0.0%
Mean3.9302095
Minimum-23.69
Maximum53.25
Zeros30
Zeros (%)< 0.1%
Negative40046
Negative (%)5.8%
Memory size5.3 MiB
2024-04-22T17:14:57.643742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-23.69
5-th percentile-14.26
Q1-5.09
median4.2
Q311.82
95-th percentile23.6
Maximum53.25
Range76.94
Interquartile range (IQR)16.91

Descriptive statistics

Standard deviation11.438532
Coefficient of variation (CV)2.9104128
Kurtosis-0.60014493
Mean3.9302095
Median Absolute Deviation (MAD)8.41
Skewness0.13062158
Sum419942.89
Variance130.84002
MonotonicityNot monotonic
2024-04-22T17:14:57.783336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.91 56
 
< 0.1%
8.6 55
 
< 0.1%
8.69 54
 
< 0.1%
5.55 53
 
< 0.1%
5.88 53
 
< 0.1%
6.26 53
 
< 0.1%
6.97 53
 
< 0.1%
5.49 51
 
< 0.1%
5.06 50
 
< 0.1%
7.23 50
 
< 0.1%
Other values (5551) 106322
 
15.3%
(Missing) 586650
84.6%
ValueCountFrequency (%)
-23.69 1
< 0.1%
-23.61 1
< 0.1%
-23.34 1
< 0.1%
-23.25 1
< 0.1%
-23.13 1
< 0.1%
-23.06 1
< 0.1%
-22.94 1
< 0.1%
-22.76 1
< 0.1%
-22.74 1
< 0.1%
-22.68 1
< 0.1%
ValueCountFrequency (%)
53.25 1
< 0.1%
51.4 1
< 0.1%
44.35 1
< 0.1%
43.43 1
< 0.1%
42.26 1
< 0.1%
42.1 1
< 0.1%
41.96 1
< 0.1%
41.47 1
< 0.1%
41.18 1
< 0.1%
40.79 1
< 0.1%

total_power
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7174
Distinct (%)5.1%
Missing552515
Missing (%)79.7%
Infinite0
Infinite (%)0.0%
Mean8.5779023
Minimum-18.81
Maximum74.4
Zeros43
Zeros (%)< 0.1%
Negative38297
Negative (%)5.5%
Memory size5.3 MiB
2024-04-22T17:14:57.917796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-18.81
5-th percentile-9.728
Q1-0.81
median7.31
Q316.43
95-th percentile30.64
Maximum74.4
Range93.21
Interquartile range (IQR)17.24

Descriptive statistics

Standard deviation12.747113
Coefficient of variation (CV)1.4860408
Kurtosis0.63771601
Mean8.5779023
Median Absolute Deviation (MAD)8.57
Skewness0.6648696
Sum1209355.6
Variance162.48888
MonotonicityNot monotonic
2024-04-22T17:14:58.043152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.36 68
 
< 0.1%
7.41 67
 
< 0.1%
23.32 66
 
< 0.1%
3.54 65
 
< 0.1%
6.79 64
 
< 0.1%
23.33 64
 
< 0.1%
23.34 63
 
< 0.1%
23.35 62
 
< 0.1%
9.91 62
 
< 0.1%
7.99 62
 
< 0.1%
Other values (7164) 140342
 
20.2%
(Missing) 552515
79.7%
ValueCountFrequency (%)
-18.81 1
< 0.1%
-18.77 1
< 0.1%
-18.64 1
< 0.1%
-18.62 1
< 0.1%
-18.38 1
< 0.1%
-18.36 1
< 0.1%
-18.31 1
< 0.1%
-18.22 1
< 0.1%
-18.2 2
< 0.1%
-18.17 1
< 0.1%
ValueCountFrequency (%)
74.4 1
< 0.1%
73.74 1
< 0.1%
72.78 1
< 0.1%
71.12 1
< 0.1%
70.64 1
< 0.1%
70.36 1
< 0.1%
69.54 1
< 0.1%
69.37 1
< 0.1%
69.16 1
< 0.1%
68.93 1
< 0.1%

velocity
Real number (ℝ)

MISSING 

Distinct805
Distinct (%)1.7%
Missing645987
Missing (%)93.1%
Infinite0
Infinite (%)0.0%
Mean-0.11849115
Minimum-4.02
Maximum4.02
Zeros41
Zeros (%)< 0.1%
Negative24761
Negative (%)3.6%
Memory size5.3 MiB
2024-04-22T17:14:58.168032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-4.02
5-th percentile-3.68
Q1-2.31
median-0.24
Q32.07
95-th percentile3.64
Maximum4.02
Range8.04
Interquartile range (IQR)4.38

Descriptive statistics

Standard deviation2.4225201
Coefficient of variation (CV)-20.444734
Kurtosis-1.2984033
Mean-0.11849115
Median Absolute Deviation (MAD)2.18
Skewness0.069209687
Sum-5629.87
Variance5.8686035
MonotonicityNot monotonic
2024-04-22T17:14:58.318532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.64 91
 
< 0.1%
3.25 91
 
< 0.1%
-3.62 91
 
< 0.1%
-3.68 90
 
< 0.1%
-2.86 90
 
< 0.1%
-3.42 90
 
< 0.1%
2.83 89
 
< 0.1%
-2.92 87
 
< 0.1%
3.07 87
 
< 0.1%
-2.91 87
 
< 0.1%
Other values (795) 46620
 
6.7%
(Missing) 645987
93.1%
ValueCountFrequency (%)
-4.02 30
< 0.1%
-4.01 65
< 0.1%
-4 56
< 0.1%
-3.99 56
< 0.1%
-3.98 64
< 0.1%
-3.97 72
< 0.1%
-3.96 73
< 0.1%
-3.95 69
< 0.1%
-3.94 60
< 0.1%
-3.93 70
< 0.1%
ValueCountFrequency (%)
4.02 25
 
< 0.1%
4.01 68
< 0.1%
4 72
< 0.1%
3.99 62
< 0.1%
3.98 65
< 0.1%
3.97 71
< 0.1%
3.96 62
< 0.1%
3.95 61
< 0.1%
3.94 68
< 0.1%
3.93 73
< 0.1%

spectrum_width
Real number (ℝ)

MISSING 

Distinct214
Distinct (%)0.5%
Missing652573
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean0.73104796
Minimum0.01
Maximum2.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 MiB
2024-04-22T17:14:58.461905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.28
Q10.53
median0.71
Q30.91
95-th percentile1.26
Maximum2.45
Range2.44
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.30250983
Coefficient of variation (CV)0.41380298
Kurtosis0.75657234
Mean0.73104796
Median Absolute Deviation (MAD)0.19
Skewness0.45237358
Sum29919.6
Variance0.091512196
MonotonicityNot monotonic
2024-04-22T17:14:58.606963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 632
 
0.1%
0.61 615
 
0.1%
0.65 613
 
0.1%
0.76 606
 
0.1%
0.69 591
 
0.1%
0.71 584
 
0.1%
0.75 573
 
0.1%
0.59 572
 
0.1%
0.67 569
 
0.1%
0.7 559
 
0.1%
Other values (204) 35013
 
5.0%
(Missing) 652573
94.1%
ValueCountFrequency (%)
0.01 541
0.1%
0.02 2
 
< 0.1%
0.03 2
 
< 0.1%
0.04 8
 
< 0.1%
0.05 8
 
< 0.1%
0.06 6
 
< 0.1%
0.07 21
 
< 0.1%
0.08 18
 
< 0.1%
0.09 38
 
< 0.1%
0.1 29
 
< 0.1%
ValueCountFrequency (%)
2.45 1
 
< 0.1%
2.42 1
 
< 0.1%
2.27 2
< 0.1%
2.22 3
< 0.1%
2.19 1
 
< 0.1%
2.14 4
< 0.1%
2.12 2
< 0.1%
2.11 1
 
< 0.1%
2.09 2
< 0.1%
2.08 1
 
< 0.1%

time
Real number (ℝ)

Distinct156
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.093006
Minimum1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 MiB
2024-04-22T17:14:58.738740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q149
median90
Q3133
95-th percentile164
Maximum171
Range170
Interquartile range (IQR)84

Descriptive statistics

Standard deviation49.742886
Coefficient of variation (CV)0.56466328
Kurtosis-1.1675292
Mean88.093006
Median Absolute Deviation (MAD)42
Skewness-0.066255634
Sum61092500
Variance2474.3547
MonotonicityNot monotonic
2024-04-22T17:14:58.875089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 5500
 
0.8%
96 5500
 
0.8%
82 5500
 
0.8%
79 5500
 
0.8%
169 5500
 
0.8%
104 5500
 
0.8%
50 5500
 
0.8%
156 5500
 
0.8%
5 5500
 
0.8%
52 5000
 
0.7%
Other values (146) 639000
92.1%
ValueCountFrequency (%)
1 4000
0.6%
2 5000
0.7%
3 4000
0.6%
4 4500
0.6%
5 5500
0.8%
6 4000
0.6%
7 4500
0.6%
8 4000
0.6%
9 4500
0.6%
10 5000
0.7%
ValueCountFrequency (%)
171 4000
0.6%
170 4000
0.6%
169 5500
0.8%
168 4500
0.6%
167 4000
0.6%
166 4500
0.6%
165 4000
0.6%
164 5000
0.7%
163 5000
0.7%
162 4000
0.6%

Interactions

2024-04-22T17:14:54.875716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:48.721829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.628382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.552047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.504306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.346116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.143415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.976474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.998779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:48.852218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.752507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.674632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.606836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.443834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.253393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.081452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:55.121840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:48.975502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.891201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.795610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.711406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.543844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.352392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.189119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:55.225426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.074139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.993230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.893918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.817576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.643989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.449971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.298098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:55.337100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.180214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.100637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.006213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.927195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.750565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.556272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.398155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:55.434049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.271642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.194799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.164513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.024492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.840028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.652380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.547429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:55.544134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.382730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.297782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.267639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.132295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.944480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.763928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.653194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:55.666537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:49.505663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:50.424662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:51.395745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:52.233718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.040534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:53.868603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-22T17:14:54.747036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-22T17:14:58.972317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
altitudelatitudelongitudereflectivityspectrum_widthtimetotal_powervelocity
altitude1.0000.005-0.0080.3650.0410.456-0.159-0.030
latitude0.0051.000-0.036-0.140-0.010-0.152-0.0680.064
longitude-0.008-0.0361.0000.1810.0040.0300.050-0.199
reflectivity0.365-0.1400.1811.0000.129-0.1810.783-0.022
spectrum_width0.041-0.0100.0040.1291.000-0.0790.1300.042
time0.456-0.1520.030-0.181-0.0791.000-0.154-0.007
total_power-0.159-0.0680.0500.7830.130-0.1541.000-0.001
velocity-0.0300.064-0.199-0.0220.042-0.007-0.0011.000

Missing values

2024-04-22T17:14:55.816057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T17:14:56.110421image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudealtitudereflectivitytotal_powervelocityspectrum_widthtime
010.659610106.72832510.04.6723.08NaNNaN3.0
110.665005106.72833017.029.2243.99NaNNaN3.0
210.670401106.72834025.024.0036.10NaNNaN3.0
310.675796106.72834033.010.5326.90NaNNaN3.0
410.681191106.72835042.0NaN25.88NaNNaN3.0
510.686586106.72835550.0NaN22.91NaNNaN3.0
610.691982106.72836058.0NaN19.56NaNNaN3.0
710.697377106.72836066.0NaN57.62NaNNaN3.0
810.702773106.72836075.0NaN42.19NaNNaN3.0
910.708169106.72837083.0NaN55.11NaNNaN3.0
longitudelatitudealtitudereflectivitytotal_powervelocityspectrum_widthtime
69349013.291719106.68749023080.0NaNNaNNaNNaN162.0
69349113.297075106.68741623137.0NaNNaNNaNNaN162.0
69349213.302430106.68733023195.0NaNNaNNaNNaN162.0
69349313.307786106.68724023252.0NaNNaNNaNNaN162.0
69349413.313141106.68716023310.0NaNNaNNaNNaN162.0
69349513.318499106.68708023367.0NaNNaNNaNNaN162.0
69349613.323854106.68700023425.0NaNNaNNaNNaN162.0
69349713.329210106.68690523482.0NaNNaNNaNNaN162.0
69349813.334565106.68683023541.0NaNNaNNaNNaN162.0
69349913.339920106.68674523598.0NaNNaNNaNNaN162.0

Duplicate rows

Most frequently occurring

longitudelatitudealtitudereflectivitytotal_powervelocityspectrum_widthtime# duplicates
510.65961106.72832510.0NaN22.88NaNNaN37.03
610.65961106.72832510.0NaN22.93NaNNaN39.03
2910.65961106.72832510.0NaN23.28NaNNaN74.03
010.65961106.72832510.0NaN17.20NaNNaN145.02
110.65961106.72832510.0NaN17.24NaNNaN146.02
210.65961106.72832510.0NaN17.25NaNNaN146.02
310.65961106.72832510.0NaN22.86NaNNaN36.02
410.65961106.72832510.0NaN22.87NaNNaN36.02
710.65961106.72832510.0NaN22.99NaNNaN23.02
810.65961106.72832510.0NaN23.03NaNNaN15.02